"""Konfiguration: Provider-Stacks, Rollen-Auflösung, Engine-Konstanten.

Rollen-Modelle stehen AUSSCHLIESSLICH in der .env (ROLLE_<NAME>=provider:modell) —
nirgendwo im Code ein Default oder Fallback. Unkonfigurierte Rolle = harter Stopp.
"""
import os
from pathlib import Path

PROJECT_ROOT = Path(__file__).resolve().parent.parent
TEMPLATES_DIR = Path(__file__).resolve().parent / "templates"
STORAGE_DIR = PROJECT_ROOT / "storage"
DB_PATH = STORAGE_DIR / "planer.db"
LAEUFE_DIR = STORAGE_DIR / "laeufe"
OPENCODE_CONFIG = PROJECT_ROOT / "dev-ops" / "opencode-slim.json"
PROJEKTE_DATEI = PROJECT_ROOT / "projekte.txt"


def _load_env(path: Path) -> None:
    """Mini .env-Loader (keine Dependency): KEY=VALUE-Zeilen. Die DATEI gewinnt über
    vererbte Env (Creator-Lehre: vererbte Werte pinnen sonst still veraltete Stände)."""
    try:
        text = path.read_text(encoding="utf-8")
    except OSError:
        return
    for line in text.splitlines():
        line = line.strip()
        if not line or line.startswith("#") or "=" not in line:
            continue
        key, _, value = line.partition("=")
        key, value = key.strip(), value.strip().strip('"').strip("'")
        if key:
            os.environ[key] = value


_load_env(PROJECT_ROOT / ".env")

# --- Provider-Stacks: nur Aufrufweg + Auth, KEINE Modelle -------------------------
PROVIDERS = {
    "claude": {"cli": "claude", "env_key": None},        # Auth via OAuth/~/.claude (Abo)
    "minimax": {"cli": "opencode", "env_key": "MINIMAX_API_KEY"},
}

# LLM-Rollen des Scan-Boards. Jede braucht ROLLE_<NAME> in der .env.
ROLLEN = ("schneiden", "nachfass", "sichten", "judge")


def resolve_role(role: str) -> tuple[str, str]:
    """→ (provider, modell) aus ROLLE_<NAME>=provider:modell. Fehlt die Konfiguration
    oder ist sie unbrauchbar: RuntimeError — NIE ein stiller Default (Leitprinzip 2)."""
    env_name = f"ROLLE_{role.upper()}"
    wert = os.getenv(env_name, "").strip()
    if not wert:
        raise RuntimeError(
            f"Rolle '{role}' hat kein Modell konfiguriert — {env_name}=provider:modell in .env setzen")
    provider, _, modell = wert.partition(":")
    if provider not in PROVIDERS:
        raise RuntimeError(
            f"{env_name}: unbekannter Provider '{provider}' (bekannt: {', '.join(PROVIDERS)})")
    if not modell:
        raise RuntimeError(f"{env_name}: Modell fehlt (Format provider:modell)")
    return provider, modell


# --- Engine-Konstanten (Kanban + Agenten) -----------------------------------------
KANBAN_BATCH = int(os.getenv("KANBAN_BATCH", "4"))            # Karten je Paket
MAX_CARD_RETRIES = int(os.getenv("MAX_CARD_RETRIES", "3"))    # dann dead-letter
RETRY_BACKOFF = float(os.getenv("RETRY_BACKOFF", "30"))       # Basis-Sekunden, exponentiell
MAX_CONCURRENT_AGENTS = int(os.getenv("MAX_CONCURRENT_AGENTS", "6"))
MAX_CONCURRENT_AGENTS_PER_TOPIC = int(os.getenv("MAX_CONCURRENT_AGENTS_PER_TOPIC", "6"))
MAX_CONCURRENT_API_AGENTS = int(os.getenv("MAX_CONCURRENT_API_AGENTS", "12"))
RAM_MIN_FREE_PCT = int(os.getenv("RAM_MIN_FREE_PCT", "20"))   # 0 = RAM-Gate aus
AGENT_TIMEOUT = int(os.getenv("AGENT_TIMEOUT", "360"))        # Sekunden je LLM-Call